Grid Resolution Effects on LES of a Piloted Methane-Air Flame

Abstract

The grid dependence of LES of a piloted methane-air (Sandia D) flame is studied on a series of grids with progressively increased resolution reaching about 10 million cells. Chemical compositions, density and temperature fields are modeled based on the evolution of mixture fraction combined with a steady flamelet model. However, to minimize interpolation uncertainties that are routinely introduced by a standard flamelet look-up table procedure, we adopt a simple smooth analytical relationship for specific volume and temperature as functions of mixture fraction. Such an analytical relationship can be easily inferred by approximating a steady flamelet solution by quadratic functions that are known to give a quite accurate representations of the lean mixtures. The simulation results are discussed and compared with available experimental data. In particular, the dependence of LES turbulent statistics on the turbulence resolution length scale is analyzed and tested for the existence of intermediate inertial range asymptotic behavior. For the most part, the statistics converge for the finest grids, but the RMS of the mixture fraction early in the flame shows some residual grid dependence.

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Document Details

Document Type
Technical Report
Publication Date
May 20, 2009
Accession Number
AD1005631

Entities

People

  • Hao Wang
  • K. A. Kemenov
  • Stephen B. Pope

Organizations

  • Cornell University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Boltzmann Equation
  • Chemical Reactions
  • Chemistry
  • Combustion
  • Computer Programs
  • Diffusion
  • Diffusivity
  • Equations
  • Experimental Data
  • Far Field
  • Flow
  • Gas Turbines
  • High Resolution
  • Near Field
  • Specific Volume
  • Statistics
  • Turbulent Flow

Readers

  • Combustion science or combustion engineering.
  • Computational Modeling and Simulation
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms